如何组织我的数据以开发房价地理参考模型？答案

【问题标题】：How to organize my data for the development of a georeferenced model of house prices?如何组织我的数据以开发房价地理参考模型？
【发布时间】：2020-03-10 18:28:12
【问题描述】：

我有许多数据库，其中包含房屋的多种特征，例如：类型（住宅，工业），位置，楼层数，每平方米价格，物业面积。这些都是我的变量。而且我有唯一的代码编号（每个房子一个代码）。我有 17 个数据库。从 2000 年到 2017 年。每年一个 excel 文件。在每个 excel 文件中有 50 张（每个州一张），其中包含房屋的位置及其几个特征（我之前提到的那些）。所有数据库每个州都有不同数量的观察（房屋数量）。他们保留了前一年的观察结果，并在明年增加了一些。例如，在 2000 年的数据库中，在一个状态（一张 excel 表）我有 100 个观察值。但明年还有 40 次观测。

这是一张工作表（你可以在 Rstudio 中运行）：

 structure(list(Code = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 
13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 
29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 
45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 
61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 
77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 
93, 94, 95, 96, 97, 98, 99, 100, 101, 102), Denom = c("A.H.", 
"Jr.", "Jr.", "Urb.", "Urb.", "Av.", "Calle", "Malecón", "Malecón", 
"Malecón", "Urb.", "Urb.", "Urb.", "Jr.", "Calle", "Jr.", "Jr.", 
"Ovalo", "Malecón", "Malec.", "Malecón", "Malecón", "Malecón", 
"Malec.", "Av.", "Pque.", "Pque.", "Pque.", "Pque.", "Pque.", 
"Pque.", "Av.", "Av.", "Av.", "Urb.", "Cerro", "Malecón", "Urb.", 
"Malec.", "Malec.", "Urb.", "Jr.", "Urb.", "Av.", "Av.", "Av.", 
"Av.", "Urb.", "Av,", "Urb.", "Urb.", "Urb.", "Urb.", "Urb.", 
"Urb.", "Urb.", "Urb.", "Urb.", "Urb.", "Urb.", "Urb.", "Urb.", 
"Urb.", "Urb.", "Urb.", "Urb.", "Urb.", "Urb.", "Urb.", "Urb.", 
"Urb.", "Urb.", "Urb.", "Av.", "Urb.", "A.H.", "Av.", "Av.", 
"Av.", "Malecón", "Malecón", "Malecón", "Av.", "Malecón", "A.H.", 
"Urb.", "Malecón", "Malecón", "Malecón", "Malec.", "Malecón", 
"Baln.", "Baln.", "Baln.", "Baln.", "Baln.", "Urb.", "Baln.", 
"Baln.", "Urb.", "Urb.", "Urb."), Location = c("21 de Marzo", 
"Abtao Cdra. 6 / 2 de Mayo", "Abtao Frente Bungalows FAP", "Asoc. Villas de Ancón", 
"Asoc. Villas de Ancón", "Autovía Malecón Las Colinas", "Bajada Los Cangrejos Playa Hermosa", 
"Bardelli Cdra. 3", "Bardelli Cdra. 4", "Bardelli Cdra. 4", "Brisas de Santa Rosa", 
"Bungalows", "Bungalows", "Daniel A. Carrión Cdra. 4", "Daniel A. Carrión Cdra. 4", 
"Dos de Mayo Cdra. 6", "Dos de Mayo esq. Carrión", "Entrada Ancón", 
"Ferreyros Cdra. 1", "Ferreyros Cdra. 2", "Ferreyros Cdra. 3", 
"Ferreyros Cdra. 3", "Ferreyros Cdra. 5", "Ferreyros Cdra. 6", 
"Florida - Miramar", "Industrial", "Industrial", "Industrial", 
"Industrial", "Industrial", "Industrial", "La Florida Urb. Miramar", 
"La Florida Urb. Miramar", "La Florida Urb. Miramar", "La Pera", 
"Lancheros", "Las Colinas", "Las Colinas", "Las Colinas", "Las Colinas", 
"Las Colinas, Malec. Pardo", "Loa Cdra. 2", "Los Alamos", "Miramar", 
"Miramar", "Miramar", "Miramar", "Miramar", "Miramar", "Miramar 1ra. Fila", 
"Miramar 1ra. Fila", "Miramar 1ra. Fila", "Miramar 1ra. Fila", 
"Miramar 1ra. Fila", "Miramar 1ra. Fila", "Miramar 2da. Fila", 
"Miramar 2da. Fila", "Miramar 2da. Fila", "Miramar 2da. Fila", 
"Miramar 2da. Fila", "Miramar 2da. Fila", "Miramar 2da. Fila", 
"Miramar 3ra. Fila", "Miramar 4ta. Fila", "Miramar 4ta. Fila", 
"Miramar 4ta. Fila", "Miramar 4ta. Fila", "Miramar 4ta. Fila", 
"Miramar 4ta. Fila", "Miramar Av. La Florida", "Miramar Calle 23, 2da. Fila", 
"Miramar Calle Sorrento Cdra. 1", "Miramar Mz. 11,  Lote 2, 2da. Fila", 
"Miramar Urb. Miramar", "Nueva Era", "Oasis", "Panam. Norte Km. 38.5", 
"Panamericana Norte KM. 42.0", "Panamericana Norte.", "Pardo Urb. Bungalows", 
"Pardo Urb. Bungalows", "Pardo Urb. Colinas", "Parque Esq. Malec. Ferreyros", 
"Playa Hermosa", "San Francisco de Asis", "San José", "San Martín", 
"San Martín Cdra. 3", "San Martín Cdra. 4", "San Martín Cdra. 4", 
"San Martín Cdra. 6", "Santa Rosa", "Santa Rosa", "Santa Rosa", 
"Santa Rosa", "Santa Rosa", "Santa Rosa", "Santa Rosa (Fte. Mar)", 
"Santa Rosa Country Club", "Villa Estar", "Virgen del Rosario", 
"Virgen del Rosario"), `Area(M²)` = c(160, 2300, 300, 300, 600, 
5231, 398, 644, 600, 682, 120, 160, 220, 420, 428, 2310, 300, 
1450, 340, 450, 750, 600, 500, 330, 246, 1230, 922, 2600, 1800, 
3000, 7500, 233, 280, 300, 130, 112938, 313, 210, 315, 315, 200, 
600, 160, 280, 300, 280, 308, 258, 280, 258, 140, 279, 280, 389, 
280, 280, 280, 351, 290, 396, 300, 250, 160, 144, 280, 175, 150, 
308, 246, 300, 259, 140, 290, 500, 108, 120, 247, 50000, 25000, 
200, 220, 220, 410, 360, 160, 140, 450, 480, 400, 420, 530, 200, 
188, 187, 200, 190, 100, 400, 200, 160, 192, 192), `$ M²` = c(100, 
220, 220, 50, 110, 220, 400, 600, 600, 600, 130, 220, 220, 70, 
200, 400, 390, 140, 660, 590, 660, 660, 660, 550, 220, 50, 50, 
50, 50, 50, 50, 220, 220, 220, 120, 50, 170, 170, 170, 170, 170, 
300, 60, 220, 220, 220, 220, 180, 200, 210, 200, 200, 200, 200, 
200, 170, 170, 170, 170, 170, 170, 170, 130, 90, 100, 100, 100, 
90, 100, 220, 170, 70, 170, 220, 50, 100, 145, 55, 70, 200, 200, 
170, 420, 500, 70, 180, 700, 700, 700, 600, 750, 100, 100, 100, 
100, 100, 140, 150, 100, 80, 100, 100), Type = c("RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "CV Comercial", 
"RDA Residencial", "RDA Residencial", "RDA Residencial", "RDA Residencial", 
"RDA Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDA Residencial", "RDM Residencial", 
"RDM Residencial", "RDA Residencial", "RDA Residencial", "RDA Residencial", 
"RDA Residencial", "RDA Residencial", "RDA Residencial", "RDM Residencial", 
"I2 Industrial", "I2 Industrial", "I2 Industrial", "I2 Industrial", 
"I2 Industrial", "I2 Industrial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "PU Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"CZ Comercial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "CV Comercial", 
"PU Pre-Urbano", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDA Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDA Residencial", "RDA Residencial", "RDA Residencial", 
"RDA Residencial", "RDA Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial", "RDM Residencial", "RDM Residencial", "RDM Residencial", 
"RDM Residencial"), Floors = c(3, 4, 4, 3, 3, 3, 3, 15, 15, 15, 
3, 3, 3, 3, 3, 5, 5, 3, 15, 5, 15, 15, 15, 9, 5, 3, 3, 3, 3, 
3, 3, 5, 5, 5, 3, 2, 2, 2, 2, 2, 2, 5, 3, 5, 5, 5, 5, 5, 5, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 5, 3, 
3, 3, 5, 3, 3, 4, 2, 2, 2, 2, 2, 9, 5, 3, 3, 15, 15, 15, 10, 
15, 2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 3)), row.names = c(NA, -102L
), class = c("tbl_df", "tbl", "data.frame"))

所以我想开发一个 GIS 模型 Property Valuation。一个模型可以帮助那些评估房地产的人对某个州可能拥有的价格范围有一个概览。因此，我们的想法是可视化包含所有这些州的地图，并在所有地图中查看价格范围（即每平方米 500-2000 美元）。这应该由一种颜色的不同深浅来表示。深色调将是最昂贵的（州）。

所以第一步是组织数据。我想看看每个州房价的一些趋势（从 2000 年到 2017 年）和一些统计信息（平均值、中位数等）。最后，我应该如何组织我的数据以开发房价地理参考模型？我应该为每个州创建一个包含所有观察结果（所有年份）的数据库吗？每个 excel 表都将包含每个州的所有观察结果（从 2000 年到 2017 年）。

提前谢谢你:)

【问题讨论】：

标签： rstudio geospatial spatial arcgis economics

【解决方案1】：

这是一个非常广泛的问题。可能太宽泛而无法得到任何有意义的答案。对于您概述的数据集，我最大的建议是将其从 excel 中取出并使用数据库来存储数据。目前所有这些数据都在 Excel 电子表格中吗？ Excel 不是数据库。它确实存储数据并具有一些优势，但它的设置不适合处理查询。

【讨论】：

感谢您的建议，我将使用 Rstudio 来处理。